scholarly journals Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing

2014 ◽  
Vol XL-8 ◽  
pp. 1413-1416 ◽  
Author(s):  
G. Krishna ◽  
R. N. Sahoo ◽  
S. Pargal ◽  
V. K. Gupta ◽  
P. Sinha ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Winter Wheat ◽  
Cross Validation ◽  
Yellow Rust ◽  
Wheat Crop ◽  
Mean Square ◽  
Rust Disease ◽  
Winter Wheat Crop ◽  
Mlr Model

The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis) of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.) cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500 nm. The partial least squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a coefficient of determination (R2) of 0.96, a standard error of cross validation (SECV) of 12.74 and a root mean square error of cross validation (RMSECV) of 12.41. The validation analysis of this PLS model yielded r2 as 0.93 with a SEP (Standard Error of Prediction) of 7.80 and a RMSEP (Root Mean Square Error of prediction) of 7.46. The loading weights of latent variables from PLS model were used to identify sensitive wavelengths. To assess their suitability multiple linear regression (MLR) model was applied on these wavelengths which resulted in a MLR model with three identified wavelength bands (428 nm, 672 nm and 1399 nm). MLR model yielded acceptable results in the form of r2 as 0.89 for calibration and 0.90 for validation with SEP of 3.90 and RMSEP of 3.70. The result showed that the developed model had a great potential for precise delineation and detection of yellow rust disease in winter wheat crop.

scholarly journals Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 19
Author(s):  
Jiří Mezera ◽  
Vojtěch Lukas ◽  
Igor Horniaček ◽  
Vladimír Smutný ◽  
Jakub Elbl
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Nitrogen Fertilizers ◽  
Wheat Crop ◽  
Significant Difference ◽  
Winter Wheat Crop ◽  
The Individual ◽  
Sentinel 2

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Effect of Winter Wheat Crop Residue on No-Till Corn Growth and Development

2007 ◽  
Vol 99 (2) ◽  
pp. 549-555 ◽  
Author(s):  
Anatoliy G. Kravchenko ◽  
Kurt D. Thelen
Keyword(s):  
Winter Wheat ◽  
Growth And Development ◽  
Crop Residue ◽  
Wheat Crop ◽  
No Till ◽  
Winter Wheat Crop

scholarly journals Winter wheat crop water consumption and its effect on yields in southern Romania, in the very dry 2019-2020 agricultural year

2021 ◽  
Vol 49 (2) ◽  
pp. 12309
Author(s):  
Mihai BERCA ◽  
Valentina-Ofelia ROBESCU ◽  
Roxana HOROIAS
Keyword(s):  
Winter Wheat ◽  
Water Consumption ◽  
Soil Depth ◽  
Vegetation Period ◽  
Early Summer ◽  
Wheat Crop ◽  
Soil Drought ◽  
Crop Water ◽  
Winter Wheat Crop ◽  
Crop Water Consumption

Researches on winter wheat in the south part of Romanian Plain during the dry years 2019 and 2020 have been focused on the crop water consumption issue in excessive conditions of air and soil drought. The wheat crop water consumption in the research sites (Calarasi and Teleorman counties), for the entire vegetation period, autumn – spring – summer, is between 1000 and 1050 m3 of water for each ton of wheat produced. Only in the spring-summer period, the wheat extracts a quantity of about 5960 m3 ha-1, i.e. 851 m3 t-1. The useful water reserve is normally located at about 1500 m3/ha-1, at a soil depth of 0-150 cm. In the spring of 2020, it has been below 400 m3 ha-1, so that at the beginning of May the soil moisture had almost reached the wilting coefficient (WC). Wheat plants have been able to survive the thermal and water shock of late spring - early summer, due to enhanced thermal alternation between air and soil. For a period of about 34 days, this alternation brought the plants 1-1.5 mm water, i.e. approximately 442 m3 ha-1, which allowed the prolongation of the plant’s agony until the rains of the second half of May. Yields have been, depending on the variety, between 1500 and 3000 kg ha-1, in average, covering only 60% of the crop costs. Other measures to save water in the soil have also been proposed in the paper.

The effects of CO2 database on a localized AquaCrop model construction based on the field experiment

2021 ◽  
Author(s):  
fawen li ◽  
chunya song ◽  
hua li
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Mean Square Error ◽  
North China Plain ◽  
North China ◽  
Mean Square ◽  
Simulation Results

Abstract To examine whether the use of default CO2 database affected the simulation results, this paper built the AquaCrop models of winter wheat based on the measured CO2 database and the default CO2 database, respectively. The models were calibrated with data (2017–2018) and validated with the data (2018–2019) in the North China Plain. The residual coefficient method (CRM), root mean square error (RMSE), normalized root mean square error (NRMSE) and determination coefficient (R2) were used to test the model performance. The results showed that the accuracy of simulation under the two CO2 database were both good. Compared with the default CO2 database, the simulation accuracy under the measured CO2 database had higher accuracy. In order to verify the model further, the simulated values of evapotranspiration, soil water content and measured values were compared and analyzed. The results showed that there were some errors between the measured evapotranspiration and the values of simulation in the filling and waxing period of winter wheat. In general, the simulation values of evapotranspiration were consistent with the measured value at different irrigation levels. The simulated values ​​of the soil water content at the three levels of irrigation were all higher than the measured values, but the simulated results basically reflected the dynamic changes of soil water content throughout the growth period. The model adjustment value of WP*(the normalized water productivity) were a difference under the two CO2 databases, which is one of the reasons for the difference in the simulation results. The results show that in the absence of measured CO2 data, the default CO2 database can be used, which has little influence on the model construction, and the accuracy of the model constructed meets the actual demand. The research results can provide a basis for the establishment of crop models in North China Plain.

Аgroclimatic conditions for formation of the winter wheat crop in the territory of the south federal district

2021 ◽  
Author(s):  
I. F. Asaulyak ◽  
Keyword(s):  
Winter Wheat ◽  
Federal District ◽  
Meteorological Station ◽  
Wheat Crop ◽  
The South ◽  
Winter Wheat Crop

An assessment of the dynamics of the average regional yield and the climatic component of winter wheat yields in the territory of the Southern Federal District has been carried out. The dynamics of the duration of dry and dry periods was determined according to the data of the Krasnodar meteorological station.

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